An enhanced fuzzy-genetic algorithm to solve satisfiability problems

José Francisco Saray Villamizar, Youakim Badr, Ajith Abraham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The satisfiability is a decision problem that belongs to NP-complete class and has significant applications in various areas of computer science. Several works have proposed high-performance algorithms and solvers to explore the space of variables and look for satisfying assignments. Pedrycz, Succi and Shai [1] have studied a fuzzy-genetic approach which demonstrates that a formula of variables can be satisfiable by assigning Boolean variables to partial true values between 0 and 1. In this paper we improve this approach by proposing an improved fuzzy-genetic algorithm to avoid undesired convergence of variables to 0.5. The algorithm includes a repairing function that eliminates the recursion and maintains a reasonable computational convergence and adaptable population generation. Implementation and experimental results demonstrate the enhancement of solving satisfiability problems.

Original languageEnglish (US)
Title of host publication11th International Conference on Computer Modelling and Simulation, UKSim 2009
Pages77-82
Number of pages6
DOIs
StatePublished - Sep 8 2009
Event11th International Conference on Computer Modelling and Simulation, UKSim 2009 - Cambridge, United Kingdom
Duration: Mar 25 2009Mar 27 2009

Publication series

Name11th International Conference on Computer Modelling and Simulation, UKSim 2009

Conference

Conference11th International Conference on Computer Modelling and Simulation, UKSim 2009
CountryUnited Kingdom
CityCambridge
Period3/25/093/27/09

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Modeling and Simulation

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